Data Analyst

Hymans Robertson
Glasgow
1 year ago
Applications closed

Related Jobs

View all jobs

Data Analyst

Data Analyst

Data Analyst

Data Analyst

Data Analyst

Data Analyst

The Vacancy

You will be part of the Data Journey team within our Third-Party Administration area where you will be able to display your abilities as a data enthusiast as we tackle our ambition of building a modern data platform, while serving our current customer needs.

The role will focus on the modernisation and continuous improvement of data analysis, transformation, and visualisation within this business area.

You will identify, investigate, and resolve data issues, discrepancies, and risks within new or existing datasets to ensure the datasets are complete and consistent for downstream usage. You will identify requirements and develop reports and extracts for key stakeholders both in on premise and cloud-hosted data stores. You will work as part of the Data Engineering team to analyse and develop robust data models and pipelines to support analytics, reporting and visualisations from our Data Lakehouse. You will work alongside technical and non-technical teams, understanding the user requirements and will bridge the gap between the different stakeholders. You will keep abreast of the latest developments in data tooling, promoting best practice and guidance to the wider business.

About You

You will be comfortable working as part of a team, as well as having the initiative to explore solutions on your own. In our growing data team, you will have the opportunity to build robust data and reporting solutions that can be accessed using technologies suited for the specified audience.

To succeed and enjoy this role, you will either be working with working with SQL databases such as Microsoft SQL Server, either on premise or cloud hosted, and excited about building your expertise with Azure-hosted data platforms.

To be successful in this role, you will be:

Comfortable working with large volumes of data, both time series and numeric data Experienced in the following data technologies: SQL database technologies utilising both on-premise, and cloud data platforms. Scripting, extracting, creating, and modifying data. Knowledge and understanding of SQL Objects using Stored Procedures, Views, and Functions Meticulous in your approach and can pinpoint and remedy data and reporting discrepancies from source data through to reporting. Confident in engaging constructively in a multi-disciplined team environment. Self-motivated with a drive to learn and share knowledge. An effective communicator and an effective team player. Able to forge strong and professional relationships.

Desirable:

Domain knowledge of the pensions industry would be beneficial but most important is a passion to learn. Experience or familiarity with some of the following: Coding in R, Python, or other data modelling/analysis technologies PowerBI for reporting and visualisations Data modelling for downstream extraction or consumption Data Lakes for data storage, including formats such as parquet/delta or similar technologies. Data cleansing, manipulation, and analysis of large datasets Familiarity with continuous integration, continuous delivery, agile methodologies, and Azure DevOps.

Subscribe to Future Tech Insights for the latest jobs & insights, direct to your inbox.

By subscribing, you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

Data Science Jobs for Career Switchers in Their 30s, 40s & 50s (UK Reality Check)

Thinking about switching into data science in your 30s, 40s or 50s? You’re far from alone. Across the UK, businesses are investing in data science talent to turn data into insight, support better decisions and unlock competitive advantage. But with all the hype about machine learning, Python, AI and data unicorns, it can be hard to separate real opportunities from noise. This article gives you a practical, UK-focused reality check on data science careers for mid-life career switchers — what roles really exist, what skills employers really hire for, how long retraining typically takes, what UK recruiters actually look for and how to craft a compelling career pivot story. Whether you come from finance, marketing, operations, research, project management or another field entirely, there are meaningful pathways into data science — and age itself is not the barrier many people fear.

How to Write a Data Science Job Ad That Attracts the Right People

Data science plays a critical role in how organisations across the UK make decisions, build products and gain competitive advantage. From forecasting and personalisation to risk modelling and experimentation, data scientists help translate data into insight and action. Yet many employers struggle to attract the right data science candidates. Job adverts often generate high volumes of applications, but few applicants have the mix of analytical skill, business understanding and communication ability the role actually requires. At the same time, experienced data scientists skip over adverts that feel vague, inflated or misaligned with real data science work. In most cases, the issue is not a lack of talent — it is the quality and clarity of the job advert. Data scientists are analytical, sceptical of hype and highly selective. A poorly written job ad signals unclear expectations and immature data practices. A well-written one signals credibility, focus and serious intent. This guide explains how to write a data science job ad that attracts the right people, improves applicant quality and positions your organisation as a strong data employer.

Maths for Data Science Jobs: The Only Topics You Actually Need (& How to Learn Them)

If you are applying for data science jobs in the UK, the maths can feel like a moving target. Job descriptions say “strong statistical knowledge” or “solid ML fundamentals” but they rarely tell you which topics you will actually use day to day. Here’s the truth: most UK data science roles do not require advanced pure maths. What they do require is confidence with a tight set of practical topics that come up repeatedly in modelling, experimentation, forecasting, evaluation, stakeholder comms & decision-making. This guide focuses on the only maths most data scientists keep using: Statistics for decision making (confidence intervals, hypothesis tests, power, uncertainty) Probability for real-world data (base rates, noise, sampling, Bayesian intuition) Linear algebra essentials (vectors, matrices, projections, PCA intuition) Calculus & gradients (enough to understand optimisation & backprop) Optimisation & model evaluation (loss functions, cross-validation, metrics, thresholds) You’ll also get a 6-week plan, portfolio projects & a resources section you can follow without getting pulled into unnecessary theory.